Opinion Mining: An Approach to Feature Engineering

Abstract

Sentiment Analysis or opinion mining refers to a process of identifying and categorizing the subjective information in source materials using natural language processing (NLP), text analytics and statistical linguistics. The main purpose of opinion mining is to determine the writer's attitude towards a particular topic under discussion. This is done by identifying a polarity of a particular text paragraph using different feature sets. Feature engineering in pre-processing phase plays a vital role in improving the performance of a classifier. In this paper we empirically evaluated various features weighting mechanisms against the well-established classification techniques for opinion mining, i.e. Naive Bayes-Multinomial for binary polarity cases and SVM-LIN for multiclass cases. In order to evaluates these classification techniques we use Rotten Tomatoes publically available movie reviews dataset for training the classifiers as this is widely used dataset by research community for the same purpose. The empirical experiment concludes that the feature set containing noun, verb, adverb and adjective lemmas with feature-frequency (FF) function perform better among all other feature settings with 84% and 85% correctly classified test instances for Naïve Bayes and SVM, respectively.

Comparative Analysis of KNN, Naïve Bayes and SVM Algorithms for Movie Genres Classification Based on Synopsis.

“…In addition, the Naïve Bayes classification is proven to possess high accuracy and speed once applied to massive databases [20]. Recent studies conjointly demonstrate that Naive Bayes classifiers with word presence or absence values performed higher in predicting opinion polarities analysis of text classification method of movie reviews [21].…”

Feature-Based Sentiment Analysis for Arabic Language

“…Therefore, the main task in this study is to make computers recognizing the reviewer's sentiment and this achieved by Natural Language Processing (NLP). NLP is a framework to support an interaction between computers and human languages [8].…”

“…POS tagging has been used for a long time in text classification and NLP. POS tagging differentiates syntactic meaning of words in a sentence by using some specific tags, such as tags for noun, pronoun, verb, adjective, adverb, conjunction and others [8].…”